A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

Data files and R scripts for How To... Programme in R

A complete guide to start and improve in machine learning (ML), artificial intelligence (AI) in 2024 without ANY background in the field and stay up-to-date with the latest news and state-of-the-ar…

lncRNA-screen

Code sharing for the ciprofloxacin prediction research

Numpy-Tutorial-SciPyConf-2020

Applied Computational Genomics Course at UU: Spring 2020

R for Reproducible Scientific Analysis

NGS-1 Data analysis course

This is the development home of the workflow management system Snakemake. For general information, see

Short lessons from FAS Informatics coffee hour

SEER, reimplemented in python 🐍🔮

An analysis of antibiotic resistance in the infant gut microbiome using genome-resolved metagenomics data.

R package to estimate antibiotic resistance cutoff values from ZD and MIC distributions of environmental samples

Web application to explore the Sequence Read Archive.

CMap Notebooks for LINCS 2020 Workshop

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously

RNAseq analysis notes from Ming Tang

Informatics for RNA-seq: A web resource for analysis on the cloud. Educational tutorials and working pipelines for RNA-seq analysis including an introduction to: cloud computing, critical file form…

🎓 GSU MPA/MPP course on data visualization with R and ggplot2

Technically-oriented PDF Collection (Papers, Specs, Decks, Manuals, etc)

Statistics course for JHU Genomic Data Science Sequence

This is the source code for multisamples ARGs profiling using SARG2.0 database

repo for cicese metatranscriptomics workshop, nov 2018

Data & Code associated with my tutorial on the sci-kit learn machine learning library in python

Materials for paper titled 'Predicting Future Antibiotic Susceptibility using Regression-based Methods on Longitudinal Massachusetts Antibiogram Data'